Cardiac rhythm management system with arrhythmia prediction and prevention

ABSTRACT

A cardiac rhythm management system predicts when an arrhythmia will occur and in one embodiment invokes a therapy to prevent or reduce the consequences of the arrhythmia. A cardiac arrhythmia trigger/marker is detected from a patient, and based on the trigger/marker, the system estimates a probability of a cardiac arrhythmia occurring during a predetermined future time interval. The system provides a list of triggers/markers, for which detection values are recurrently obtained at various predetermined time intervals. Based on detection values and conditional probabilities associated with the triggers/markers, a probability estimate of a future arrhythmia is computed. An arrhythmia prevention therapy is selected and activated based on the probability estimate of the future arrhythmia.

TECHNICAL FIELD

[0001] This document relates generally to cardiac rhythm managementsystems and particularly, but not by way of limitation, to a systemproviding prediction of a future arrhythmia and preventive therapy foravoiding or mitigating the predicted arrhythmia.

BACKGROUND

[0002] The human heart normally maintains its own well-ordered intrinsicrhythm through generation of stimuli by pacemaker tissue that results ina wave of depolarization that spreads through specialized conductingtissue and then into and through the myocardium. The well-orderedpropagation of electrical depolarizations through the heart causescoordinated contractions of the myocardium that results in the efficientpumping of blood. In a normally functioning heart, stimuli are generatedunder the influence of various physiological regulatory mechanisms tocause the heart to beat at a rate that maintains cardiac output at alevel sufficient to meet the metabolic needs of the body. Abnormalitiesof excitable cardiac tissue, however, can lead to abnormalities of heartrhythm that are called arrhythmias. All arrhythmias stem from one of twocauses: abnormalities of impulse generation or abnormalities of impulsepropagation. Arrhythmias can cause the heart to beat too slowly(bradycardia, or a bradyarrhythmia) or too quickly (tachycardia, or atachyarrhythmia), either of which may cause hemodynamic compromise ordeath.

[0003] Drug therapy is often effective in preventing the development ofarrhythmias and in restoring normal heart rhythms once an arrhythmia hasoccurred. However, drug therapy is not always effective for treatingparticular arrhythmias, and drug therapy usually causes side-effectsthat may be intolerable in certain patients. For such patients, analternative mode of treatment is needed. One such alternative mode oftreatment includes the use of a cardiac rhythm management systemincorporated into an implantable device that delivers therapy to theheart in the form of electrical stimuli. Such implantable devicesinclude cardiac pacemakers that deliver timed sequences of low energyelectrical stimuli, called pacing pulses, to the heart, via anintravascular leadwire or catheter (referred to as a lead) having one ormore electrodes disposed in or about the heart. Heart contractions areinitiated in response to such pacing pulses (referred to as capturingthe heart). By properly timing the delivery of pacing pulses, the heartcan be induced to contract in proper rhythm, greatly improving itsefficiency as a pump. Pacemakers are often used to treat patients withbradycardia. Pacemakers are also capable of delivering paces to theheart in such a manner that the heart rate is slowed, a pacing modereferred to as anti-tachyarrhythmia pacing.

[0004] Cardiac rhythm management systems also includecardioverter/defibrillators (ICD's) that are capable of deliveringhigher energy electrical stimuli to the heart. ICD's are often used totreat patients with tachyarrhythmias, that is, hearts that beat tooquickly. Tachyarrhythmias can cause diminished blood circulation becausethe cardiac cycle of systole (contraction) and diastole (filling) can beshortened to such an extent that insufficient blood fills the ventriclesduring diastole. Besides the potential for such hemodynamicembarrassment, tachyarrhythmias can also degrade into even more seriousarrhythmias such as fibrillation where electrical activity spreadsthrough the myocardium in a disorganized fashion so that effectivecontraction does not occur. For example, in a particular type oftachyarrhythmia, referred to as ventricular fibrillation, the heartpumps little or no blood to the body so that death occurs withinminutes. A defibrillator delivers a high energy electrical stimulus orshock to the heart to depolarize all of the myocardium and render itrefractory in order to terminate arrhythmia, allowing the heart toreestablish a normal rhythm for the efficient pumping of blood. Inaddition to ICD's and pacemakers, cardiac rhythm management systems alsoinclude pacemaker/ICD's that combine the functions of pacemakers andICD's, drug delivery devices, and any other implantable or externalsystems or devices for diagnosing, monitoring, or treating cardiacarrhythmias.

[0005] Cardiac rhythm management systems incorporated into ICD's allowtachyarrhythmias to be automatically detected and treated in a matter ofseconds. Defibrillators are usually effective at treatingtachyarrhythmias and preventing death, but such devices are not 100%effective at treating all tachyarrhythmias in all patients. As a result,some patients may still die even if the defibrillator deliversappropriate therapy. Also, some patients have frequent tachyarrhythmias,triggering frequent therapeutic shocks. This reduces the usable life ofthe implanted battery-powered device and increases the risk oftherapy-induced complications. Furthermore, even if the devicesuccessfully treats the tachyarrhythmia, the patient may loseconsciousness during the arrhythmia which can result in related seriousor even fatal injuries (e.g., falling, drowning while bathing, caraccident while driving, etc.). Thus, there is a need for a cardiacrhythm management system that predicts when an arrhythmia will occur andinvokes a therapy to prevent or reduce the consequences of thearrhythmia.

SUMMARY

[0006] The present invention relates to a system and method forpredicting cardiac arrhythmias. In a particular embodiment, the systemand method are implemented in an implantable cardiac device having oneor more sensing channels for detecting conditioning events (e.g.,marker/trigger events as defined below) and the capability of deliveringsome type of preventive arrhythmia therapy when conditions warrant it.

[0007] In accordance with the invention, an arrhythmia is predictedby: 1) detecting a conditioning event statistically associated with theoccurrence of an arrhythmia in a patient's heart; 2) computing aconditional arrhythmia probability for the conditioning event from pastobservations of instances in which the conditioning event occurs aloneor together with an arrhythmia within a specified time period; 3)computing an estimated arrhythmia probability based upon the detectedoccurrence of the conditioning event; and 4) predicting the occurrenceof an arrhythmia within a specified prediction time period if theestimated arrhythmia probability exceeds a specified threshold value.

[0008] Conditioning events may be broadly classified into markers andtriggers. A marker event corresponds to detected a physiological statethat is statistically associated with occurrence of cardiac arrhythmias,but the causal relationship between the marker and the arrhythmia is notknown. A conditioning event is regarded as a trigger, on the other hand,if the event is thought to increase the risk of an arrhythmia occurringvia a depolarization that serves as a source for the arrhythmia.Conditioning events may be detected on a beat-to-beat basis or over alonger time frame. Examples of conditioning events include a detectedspecific morphology of a waveform representing the electrical activityof the heart, a specific pattern of activation times of different areasof the heart as sensed by a plurality of electrodes, a specific sequencepattern of heartbeats with respect to time, a value of a measuredphysiological variable such as heart rate or blood pressure, or astatistic based upon a history of occurrences of conditioning events.

[0009] In one embodiment, the conditional arrhythmia probability iscalculated as a ratio of the number of observed instances in which theconditioning event is followed by an arrhythmia within a specified basictime period, to the total number of observed instances of theconditioning event. In that case, the estimated arrhythmia probabilityfor an arrhythmia to occur within the specified basic time period afterdetection of the conditioning event is simply the calculated conditionalarrhythmia probability.

[0010] In another embodiment, the conditional arrhythmia probability CPis calculated by the expression:

CP=1−e ^(−RT)

[0011] which assumes a Poisson probability distribution, where T is ameasure of the specified prediction time period, and R is an estimate ofthe rate at which arrhythmias occur while the conditioning event ispresent. The rate R is a ratio of: 1) the number of instances in whichthe conditioning event is followed by an arrhythmia within a specifiedbasic time period, to 2) the length of the basic time period multipliedby the total number of basic time periods in which the conditioningevent is observed. The estimated arrhythmia probability for anarrhythmia to occur within the time T after detection of theconditioning event is again the conditional arrhythmia probability.Calculating the conditional arrhythmia probability in this manner allowsthe prediction time period T to differ from the length of the basic timeperiod used to derive the conditional arrhythmia probability.

[0012] In another embodiment, rather than basing the estimatedarrhythmia probability upon the detection of a conditioning event, arate at which the conditioning event occurs is detected over some periodof time. The estimated arrhythmia probability is then calculated as theproduct of an estimated probability that a conditioning event will occurtimes the probability of an arrhythmia occurring within specified timeperiod given the occurrence of the conditioning event (i.e. theconditional arrhythmia probability). Thus:

estimated arrhythmia probability=(1−e ^(−RT))(1−e ^(−CT))

[0013] where T is a measure of the specified prediction time period, Ris an estimate of the rate at which arrhythmias occur while theconditioning event is present, and C is an estimate of the rate at whichthe conditioning event occurs.

[0014] Another way of deriving a conditional arrhythmia probability,especially for trigger-types of conditioning events (although it can beused with any type of conditioning event), is to designate a particulardetected trigger event as being responsible for causing a detectedarrhythmia. Such culpability may be assigned based, e.g., upon theproximity in time between the trigger event and the onset of thearrhythmia, the magnitude of the detected trigger, or the frequency ofoccurrence of the trigger event within a specific time period prior tothe onset of the arrhythmia. A conditional arrhythmia probability CP forthat trigger event can then be calculated as a ratio of the number ofinstances in which the trigger event was deemed culpable for causing anarrhythmia, to the total number of occurrences of the trigger event.Also, as above, rather than basing the estimated arrhythmia probabilityupon the detection of the trigger event, a rate at which the triggerevent occurs can be detected over some period of time. The estimatedarrhythmia probability is then calculated as the product of an estimatedprobability that a trigger event will occur times the probability CP ofan arrhythmia occurring within a specified time period T given theoccurrence of the trigger event. Thus:

estimated arrhythmia probability=CP×(1−e ^(−CT))

[0015] In a preferred embodiment, the prediction of arrhythmias is basedupon a plurality of the same or different detected conditioning events.A composite estimated arrhythmia probability is then computed as acombination of the estimated arrhythmia probabilities derived for eachseparately detected conditioning event. The separately detectedconditioning events may be separate occurrences of the same or differentconditioning events. As before, the composite arrhythmia probability iscompared with a threshold value in order to predict the occurrence of anarrhythmia. In one embodiment, the composite arrhythmia probability iscalculated by adding the individual estimated arrhythmia probabilitiesderived for each detected conditioning event, which thus assumes eachindividual arrhythmia probability to correspond to an independent event.In other embodiments, specific combinations of detected conditioningevents are mapped in a non-linear fashion to estimated arrhythmiaprobabilities that can be added or otherwise combined with otherestimated arrhythmia probabilities to give a composite value. In stillother embodiments, the estimated arrhythmia probability is computed froma combination of conditional arrhythmia probabilities derived usingdifferent basic time periods but for the same prediction time period.

[0016] The past observations of the occurrences of conditioning eventsand arrhythmias from which the conditional arrhythmia probabilities arederived can be taken from either population data or from data collectedin real-time from a particular patient. In a preferred embodiment, theconditional arrhythmia probabilities are based initially upon pastobservations of the occurrences of events and arrhythmias taken frompopulation data, and each probability is subsequently updated from aprevious value to a present value with observations taken in real-timefrom a particular patient. In one embodiment, a conditional arrhythmiaprobability is updated only if the present value differs by apredetermined amount from the previous value. In another embodiment, theamount by which the present value differs from the previous value istested for statistical significance before a conditional arrhythmiaprobability is updated. In another embodiment, the previous value of theconditional arrhythmia probability is incremented or decremented by aspecific amount after a prediction time period in accordance withwhether the arrhythmia occurred or not, respectively.

[0017] In still another embodiment, the statistical association betweenthe conditioning event and the occurrence of an arrhythmia isperiodically reevaluated using the most recent patient-specific data. Ifthe statistical association (e.g., as a calculated from a chi-squaretest) is found to be below a specified value, the use of thatconditional arrhythmia probability in deriving a composite estimatedarrhythmia probability is discontinued.

[0018] As aforesaid, one embodiment of the invention involves deliveringpreventive arrhythmia therapy if the estimated probability of anarrhythmia occurring within a specified time period (i.e., the compositeestimated arrhythmia probability) exceeds a threshold value so that anarrhythmia can be predicted with some degree of certainty. Examples ofsuch therapies capable of being delivered by an implantable deviceinclude the delivery of pharmacologic agents, pacing the heart in aparticular mode, delivery of cardioversion/defibrillation shocks to theheart, or neural stimulation (e.g., stimulation of either thesympathetic or parasympathetic branches of the autonomic nervoussystem). Another type of therapy capable of being delivered by animplantable device in accordance with the invention (interpreting theterm “therapy” in a broad sense) is issuance of a warning signal that anarrhythmia has been predicted, which warning signal may take the form ofa audible signal, a radio-transmitted signal, or any other type ofsignal that would alert the patient or physician to the possibility ofan impending arrhythmia. Such a warning signal would allow the patientto take precautionary measures and/or allow a treating physician to takeother therapeutic steps if deemed appropriate. In accordance with theinvention, the selection of a particular therapy to be delivered or notis based upon ascertaining a physiologic state of the patient anddeciding whether or not a particular available modality of therapy isappropriate for delivery to the patient in that physiologic state.

[0019] The modality selection process in one embodiment takes the formof a matrix mapping of a state vector representing a specificphysiologic state (as determined by, e.g., the particular conditioningevents used to make the arrhythmia prediction, the prediction timeperiod for the estimated arrhythmia probability, the presence or not ofspecific conditioning events within a specified prior time period, orthe magnitude and/or presence other detected and/or calculatedvariables) to a specific therapy or therapies considered mostappropriate for preventing an arrhythmia in that instance (i.e., a pointin “therapy space”). The matrix mapping is performed using a therapymatrix containing information relating to whether or not (and/or to whatextent) a particular therapy modality is expected to be effective for agiven physiologic state. The elements of the therapy matrix may thusconstitute variables representing the appropriateness of a particulartherapy modality given the presence of a specific element in thephysiological state vector.

[0020] In one particular embodiment, a prediction scheduler makesseparate predictions for each time period in which an arrhythmia may ormay not be predicted to occur (i.e., the prediction time period) andthen makes a therapy decision using a therapy matrix specific for thatprediction time period. In this manner, a therapy decision for a giventherapy modality may be made with respect to the time period appropriatefor that modality. For example, some therapy modalities can be expectedto be effective in a short time period and are thus suitable forpreventing an arrhythmia with a short prediction time period, whileothers would not be expected to be effective until after a longer timeinterval and therefore would only be suitable if the prediction timeperiod were commensurate. In another embodiment, therapy decisions andpredictions are made at time intervals without regard to the arrhythmiaprediction periods, and the therapy matrix takes into account theprediction time period for the estimated arrhythmia probability. Forexample, the prediction time period may be incorporated into thephysiologic state vector, and the therapy matrix then containsinformation related to the appropriateness of each available therapymodality for a given prediction time period.

[0021] Rather than estimating a probability for the occurrence of anyarrhythmia, separate estimated arrhythmia probabilities can be computedfor different types of arrhythmias in accordance with the invention. Bycomputing such separate probabilities, a more informed decision as towhat mode of therapeutic intervention to employ may be made. The mostbeneficial set of separate estimated arrhythmia probabilities for anyparticular patient be expected to vary. Accordingly, selection of thearrhythmia types for which separate estimated arrhythmia probabilitiesare computed can be done dynamically by tabulating the number ofdetected occurrences of each specific type of arrhythmia and computingseparate estimated arrhythmia probabilities for those arrhythmia typesoccurring most frequently in the patient. Separate estimated arrhythmiaprobabilities can also be computed for different types of triggerevents. Such trigger events can be expected to be patient-specific, andthe particular mix of trigger events for which separate arrhythmiaprobabilities are calculated can be selected dynamically as describedabove.

BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In the drawings, like numerals describe substantially similarcomponents throughout the several views. Like numerals having differentletter suffixes represent different instances of substantially similarcomponents.

[0023]FIG. 1 is a schematic drawing illustrating generally oneembodiment of portions of a cardiac rhythm management system and anenvironment in which it is used.

[0024]FIG. 2 is a schematic drawing illustrating generally oneembodiment of a cardiac rhythm management device coupled by leads to aheart.

[0025]FIG. 3 is a schematic diagram illustrating generally oneembodiment of portions of a cardiac rhythm management device, which iscoupled to a heart and/or other portions of the patient's body.

[0026]FIG. 4A is a block diagram illustrating generally one conceptualembodiment of portions of a trigger/marker module.

[0027]FIG. 4B is a schematic diagram illustrating generally oneembodiment in which three electrode signals (e.g., RV, RM, LV) are used.

[0028]FIG. 5 is a timing diagram illustrating generally one embodimentof updating a heart rate variability marker.

[0029]FIG. 6 is a block diagram illustrating generally one conceptualembodiment of portions of an arrhythmia prediction module.

[0030]FIG. 7 is a block diagram illustrating generally one conceptualembodiment of portions of a preventive therapy control module.

[0031]FIG. 8 is a diagram illustrating generally one embodiment of atranslation matrix.

DETAILED DESCRIPTION

[0032] This invention relates to a cardiac rhythm management system thatpredicts when an arrhythmia will occur based upon the detection ofconditioning events found to be statistically associated with theoccurrences of arrhythmias. A further embodiment uses the prediction toinvoke a therapy to prevent or reduce the consequences of thearrhythmia. The present system may incorporate several featuresincluding: (1) distinguishing a degree of risk for occurrence of afuture arrhythmia, (2) making predictions that apply to a well definedfuture time interval, (3) basing predictions on direct observations ofarrhythmic triggers/markers (i.e., conditioning events) usingconditional probabilities that these arrhythmias will occur given thepast and present occurrences of the arrhythmias and thetriggers/markers, (4) assessing a confidence in the accuracy of theprediction, (5) adapting its prediction and prevention capabilities tothe individual patient, (6) assessing the expected effect of one ormultiple preventive therapies in order to determine if the likelihoodfor future arrhythmias is increased or decreased by the proposedpreventive therapies, (7) selecting a therapy based on this assessmentof its expected effect, and (8) determining whether the selectedpreventive therapy combination should be invoked based on the magnitude,timing and confidence in the arrhythmia prediction and of the expectedeffect of the selected therapy. In one embodiment, the system alsoincludes an external programmer device that can control, load andretrieve information from the implanted cardiac rhythm managementdevice, and which can process and display information obtained from theimplanted device. The present system provides techniques for predictingand preventing future cardiac arrhythmias, and such techniques can beused in combination with pacing, defibrillation, and other therapytechniques for treating presently existing cardiac arrhythmias.

[0033] In the following detailed description, reference is made to theaccompanying drawings which form a part hereof, and in which is shown byway of illustration specific embodiments in which the invention may bepracticed. These embodiments are described in sufficient detail toenable those skilled in the art to practice the invention, and it is tobe understood that the embodiments may be combined, or that otherembodiments may be utilized and that structural, logical and electricalchanges may be made without departing from the spirit and scope of thepresent invention. The following detailed description is, therefore, notto be taken in a limiting sense, and the scope of the present inventionis defined by the appended claims and their equivalents.

[0034] The present methods and apparatus will be described inapplications involving implantable medical devices including, but notlimited to, implantable cardiac rhythm management systems such aspacemakers, cardioverter/defibrillators, pacemaker/defibrillators, andbiventricular or other multi-site coordination devices. However, it isunderstood that the present methods and apparatus may also be employedin unimplanted devices of the same sort, as well as in externalmonitors, programmers and recorders.

[0035] Herein, the term “prediction” is used to mean a probabilitystatement regarding whether or not an arrhythmia will occur at a futuretime. The term “trigger” refers to one or more cardiac events, such asdepolarizations or other intrinsic heart activity signals, that maytrigger an arrhythmia. Examples of such triggers include, by way ofexample, but not by way of limitation: sinus beats, premature sinusbeats, beats following long sinus pauses; long-short beat sequences, Ron T-wave beats, ectopic ventricular beats, and premature ventricularbeats. The term “marker” refers to one or more quantities associatedwith at least one abnormal physiologic state (e.g., the occurrence of anarrhythmia) in which the quantity is obtainable from one or moreelectrophysiologic signals, or from one or more signals from one or moreother sensors. Examples of such markers include, by way of example, butnot by way of limitation: ST elevations, heart rate, increased ordecreased heart rate, abnormal heart rate variability, late-potentials,or abnormal autonomic activity.

[0036] Overview

[0037]FIG. 1 is a schematic drawing illustrating generally, by way ofexample, but not by way of limitation, one embodiment of portions of acardiac rhythm management system 100 and an environment in which it isused. In FIG. 1, system 100 includes an implantable cardiac rhythmmanagement device 105, also referred to as an electronics unit, which iscoupled by an intravascular endocardial lead 110, or other lead, to aheart 115 of patient 120. System 100 also includes an externalprogrammer 125 providing wireless communication with device 105 using atelemetry device 130. In one embodiment, external programmer 125includes a visual or other display for providing information to a userregarding operation of implanted device 105. Catheter lead 110 includesa proximal end 135, which is coupled to device 105, and a distal end140, which is coupled to one or more portions of heart 115.

[0038]FIG. 2 is a schematic drawing illustrating generally, by way ofexample, but not by way of limitation, one embodiment of device 105coupled by leads 110A-C to heart 115, which includes a right atrium200A, a left atrium 200B, a right ventricle 205A, a left ventricle 205B,and a coronary sinus 220 extending from right atrium 200A. In thisembodiment, atrial lead 110A includes electrodes (electrical contacts)disposed in, around, or near an atrium 200 of heart 115, such as ringelectrode 225 and tip electrode 230, for sensing signals and/ordelivering pacing therapy to the atrium 200. Lead 110A optionally alsoincludes additional electrodes, such as for delivering atrial and/orventricular cardioversion/defibrillation and/or pacing therapy to heart115.

[0039] In FIG. 2, a right ventricular lead 110B includes one or moreelectrodes, such as tip electrode 235 and ring electrode 240, forsensing signals and/or delivering pacing therapy. Lead 110B optionallyalso includes additional electrodes, such as coil electrodes 245A-B fordelivering right atrial and/or right ventricularcardioversion/defibrillation and/or pacing therapy to heart 115. In oneembodiment, system 100 also includes a left ventricular lead 110C, whichprovides one or more electrodes such as tip electrode 246 and ringelectrode 247, for sensing signals and/or delivering pacing therapy.Lead 110C optionally also includes one or more additional electrodes,such as coil electrodes 248A-B for delivering left atrial and/or leftventricular cardioversion/defibrillation and/or pacing therapy to heart115.

[0040] In FIG. 2, device 105 includes components that are enclosed in ahermetically-sealed enclosure, such as can 250. Additional electrodesmay be located on the can 250, or on an insulating header 255, or onother portions of device 105, for providing unipolar pacing and/ordefibrillation energy in conjunction with the electrodes disposed on oraround heart 115. Other forms of electrodes include meshes and patcheswhich may be applied to portions of heart 115 or which may be implantedin other areas of the body to help direct electrical currents producedby device 105. For example, an electrode on header 255 may be used tostimulate local muscle to provide an alert/warning to the patient. Inanother example, an additional lead is used to provide electrodesassociated with nerves or nerve ganglia, such as the vagus nerve, leftor right stellate ganglion, carotid sinus nerve, or the fat pad over theatrioventricular node. The present method and apparatus will work in avariety of configurations and with a variety of electrical contacts orelectrodes.

[0041]FIG. 3 is a schematic diagram illustrating generally, by way ofexample, but not by way of limitation, one embodiment of portions ofdevice 105, which is coupled to heart 115 and/or other portions of thepatient's body. FIG. 3 illustrates one conceptualization of variousmodules, which are implemented either in hardware or as one or moresequences of steps carried out on a microprocessor or other controller.Such modules are illustrated separately for conceptual clarity; it isunderstood that the various modules of FIG. 3 need not be separatelyembodied, but may be combined and/or otherwise implemented, such as insoftware/firmware. In FIG. 3, device 105 includes, among other things, apower source 300, such as a battery. A communication module 305 includesa telemetry or other circuit by which implantable device 105communicates with external programmer 125. A sensing module 310 sensesintrinsic heart activity signals from one or more electrodes associatedwith heart 115. In one embodiment, sensing module 310 also senses otherelectrophysiological signals. A therapy module 315 provides therapy fortreatment of present arrhythmias and prevention of future arrhythmias.In one embodiment, such therapy is provided at electrodes associatedwith heart 115 or portions of the nervous system such as, for examplebut without limitation, to sense other electrophysiological signals suchas activity from sympathetic or parasympathetic members of the autonomicnervous system, or to sense blood temperature or blood flow. In variousembodiments such therapy includes, among other things, pacing pulses,anti-tachyarrhythmia pacing (ATP), defibrillation shocks, etc.

[0042] In FIG. 3, sensing module 320 includes, among other things, oneor more sensors, such as an accelerometer, acoustic sensor, respirationand/or stroke volume sensor (e.g., using transthoracic impedance), timeand/or date of detected arrhythmia(s), cardiac displacement or bloodvessel dimensions (e.g., using ultrasonic transit time measurements), orblood electrolyte levels (e.g., using ion-selective membranes). Sensingmodule 320 also includes interface circuits that receive control signalsand preprocess the sensor signal(s). Device 105 also includes a controlcircuit, such as a microprocessor or other controller 325, whichcommunicates with various peripheral circuits via one or more nodes,such as bus 330. Controller 325 includes various functional blocks, oneconceptualization of which is illustrated in FIG. 3.

[0043] In one embodiment, controller 325 includes a bradyarrhythmiacontrol module 335 that detects bradyarrhythmias based at least in parton one or more signals obtained from sensing module 310. Bradyarrhythmiacontrol module 335 also provides one or more physiologically appropriateanti-bradyarrhythmia therapies to treat presently existingbradyarrhythmias. A tachyarrhythmia control module 340 detectstachyarrhythmias based at least in part on one or more signals obtainedfrom sensing module 310. Tachyarrhythmia control module 340 alsoprovides one or more physiologically appropriate anti-tachyarrhythmiatherapies to treat presently existing tachyarrhythmias.

[0044] For predicting and preventing arrhythmias, controller 325 alsoincludes trigger/marker module 345, arrhythmia prediction module 350,and preventive therapy control module 355. Trigger/marker module 345detects one or more triggers and/or markers based at least in part onsignals received from sensing module 310 and/or sensing module 320.Arrhythmia prediction module 450 predicts the likelihood of futurearrhythmias using probability calculations based on trigger/markerinformation received from trigger/marker module 345. Preventive therapycontrol module 355 selects the most appropriate therapy (or combinationof therapies) for preventing the future arrhythmia from a set ofavailable preventive therapies. Preventive therapy control module 355also triggers the delivery of such therapy after determining if theprobability of arrhythmia, computed by arrhythmia prediction module 350,and the expected outcome of the selected therapy warrants administrationof the therapy by therapy module 315. Thus, device 105 predicts andprevents future arrhythmias, as described more particularly below. Inone embodiment, the prediction and prevention of future arrhythmias isdeactivated if one or more present arrhythmias (e.g., ventriculartachyarrhythmia (VT) or ventricular fibrillation (VF)) are present andare being treated by techniques, known to one skilled in the art, usingtachyarrhythmia control module 340 and/or bradyarrhythmia control module335.

[0045] Example of Detecting Arrhythmogenic Trigger(s)/Marker(s)

[0046]FIG. 4A is a block diagram illustrating generally, by way ofexample, but not by way of limitation, one conceptual embodiment ofportions of trigger/marker module 345. In this embodiment,trigger/marker module 345 includes beat classification module 400,detection processing module 405, and a trigger/marker data bank such astrigger/marker list 410. Trigger/marker module 345 recurrently examinessignals from sensing module 310 and/or sensing module 320 and detectsthe presence, timing, and (if appropriate) magnitude oftriggers/markers. These detections, timings, and magnitudes are outputto detection processing module 405 either for each heartbeat, orcorresponding to a time period encompassing multiple heartbeats.

[0047] Beat classification module 400 examines signals from variouslylocated electrodes. On a beat-to-beat basis, beat classification module400 distinguishes between depolarization events that spread over theheart normally from those that spread out over the heart abnormally.Normal beats refer to those beats that are most prevalent andphysiologically sound in the patient, even though the patient's mostprevalent beat type might not be considered normal when compared to thepropagation of a depolarization in a healthy heart. In one embodiment,sensing module 310 provides and beat classification module analyzessignals from: a right atrial sensing electrode (RA), a right ventricularsensing electrode (RV), a left ventricular (LV) sensing electrode, aright sided morphology (RM) signal between defibrillation electrodes inthe right ventricular and superior vena cava and/or the metallic shellof the device, a left sided morphology (LM) signal between a leftventricular electrode and one or more of the other electrodes. Severalexample methods of operating beat classification module 400 to classifyheart beats as normal or abnormal are described below by way of example,but not by way of limitation.

[0048] A first method to distinguish normal and abnormal beats detectsdepolarizations on a plurality of electrode signals for each beat. Inone example of this method, an RV depolarization indicates that a beathas occurred. The time intervals between detection of the samedepolarization at the various electrodes (RA, RV, LV, RM, LM) reflectthe pattern and timing with which this depolarization propagates overthe heart. FIG. 4B is a schematic diagram illustrating generally, by wayof example, but not by way of limitation, one embodiment in which 3electrode signals (e.g., RV, RM, LV) are used. The sensed depolarizationon each electrode signal resets 3 interval timers (total of 9 timers)that measure time intervals initiated by the resetting event. The senseddepolarization on each electrode signal also latches the current valuein 3 timers, each of which is reset by a different electrode signal, asillustrated in FIG. 4B. In this way, each time a depolarization passesover the heart, the set of 9 time intervals is obtained. These timeintervals represent the pattern with which the depolarization reacheddifferent electrodes. Similarly, if 4 signals are used, then 16intervals exist, and if 5 signals are used, then 25 intervals exist,etc. If the set of intervals for a particular beat fall outside oflimits deemed normal, then that beat is classified as abnormal. In oneembodiment, the abnormal limits vary with the heart rate. The abnormallimits may be programmed into the device as either population-based orpatient-specific values.

[0049] A second method to distinguish normal beats from abnormal beatscompares the morphology of one or more available signals (e.g., RVand/or RM and/or LM signals) with corresponding template morphologiesfor a normal beat. In one embodiment, an RV depolarization detectionprovides a triggering event. For each RV depolarization, a correlationcoefficient between the selected morphology signal and a correspondingtemplate morphology is computed. If the coefficient is smaller than apredetermined threshold value, then the current beat is classified asabnormal. In one embodiment, different template morphologies are usedfor different ranges of heart rates. Also, the threshold values may beprogrammed into the device using either population-based orpatient-specific values.

[0050] A third method to distinguish normal and abnormal beats extractsfeatures from one or more available signals and compares the extractedfeatures with similar features for normal beats. Possible featuresinclude, by way of example, but not by way of limitation, QRS duration,R-wave amplitude, QT interval, and T-wave amplitude. For a particularbeat, if the detected value of one or more features falls outside limitsdeemed normal, then the beat is classified as abnormal. In oneembodiment, the normal limits vary with heart rate. Also, the normallimits may be programmed into the device either as population-based orpatient-specific values.

[0051] In addition to these methods of classifying beats, other methodsof identifying or classifying beats will also be suitable. Also, beatclassification module 400 can also be used with lead configurationshaving a different number of electrodes or electrodes located indifferent locations. After beat classification module 400 classifies adetected heart beat, detection processing module 405 performs theprocessing required for trigger/marker detection.

[0052] In one embodiment, detection processing module 405 uses thebeat-to-beat intervals and morphological data extracted from the signalsby beat classification module 400 and also extracts any additionalmorphological measures required for trigger/marker detection. Detectionprocessing module 405 is also capable of examining activity,respiration, and stroke volume signals received from one or moreacceleration, acoustic and/or impedance sensors and provided by sensingmodule 320.

[0053] Device 105 includes a trigger/marker list 410, which lists thetriggers/markers to be detected. List 410 includes members, for example,the types and natures of the particular trigger/marker sought to bedetected. In one embodiment, each member of the list includes acorresponding predetermined comparison value. In one example, list 410includes a member “long QRS duration” trigger/marker. For that member,list 410 also includes a comparison value indicating which QRS durationsare long. In another example, list 410 includes a particular“short-long-short sequence” of intervals between beats classified asnormal. For that member, list 410 also includes corresponding comparisonvalues establishing the criteria for long and short intervals in thesequence. Alternatively, list 410 includes members that include one ormore beats classified as abnormal.

[0054] In one embodiment, the type and nature of the triggers/markers inlist 410 are obtained from research in the relevant clinical population.Several different types of triggers/markers exist. For example, a firstbasic type of triggers/markers, is referred to as “sequence type”triggers/markers. Sequence type triggers/markers are based on sequencesof one or more beats. Examples of sequence type triggers/markersinclude, by way of example, but not by way of limitation, prematurenormal beats, premature abnormal beats, delayed normal beats, delayedabnormal beats, long-short intervals between beats, short-long intervalsbetween beats, and multiple (couplets, triplets, etc) abnormal beats.Detection processing module 405 detects and counts the number of timeseach of the specified sequence type triggers/markers are present duringa particular observation period.

[0055] An example of a second basic type of triggers/markers is referredto as “value type” triggers/markers. Value type triggers/markers arebased on metrics from the available signals, which are compared topredetermined comparison values to determine whether the trigger/markeris present during a particular observation period. Examples of valuetype triggers/markers based on morphology intrinsic heart activitysignals include, by way of example, but not by way of limitation, QRSduration, ST magnitude, QT interval, and R-wave amplitude. In oneembodiment, detection of morphology-based value type triggers/markers isbased only on consideration of one or more beats classified as normal.In one example, a trigger/marker is based on the most recent beat. Inanother example, the trigger/marker is based on an average of morphologyvalues over some number of previous normal beats. In a further example,the trigger/marker is based on one or more abnormal beats. Otherexamples of value type triggers/markers are not based on the heart'selectrical activity. Such value type triggers/markers include, by way ofexample, but not by way of limitation, present respiratory rate, aposition in the respiratory cycle at which the beat is detected, tidalvolume of a respiratory inhalation or exhalation, cardiac stroke volume,present activity level of the patient, or the current position withinthe diurnal cycle.

[0056] An example of a third basic type of triggers/markers is referredto as “history type” triggers/markers. In one embodiment, the presenceor absence of history type triggers/markers is determined based on oneor more possible computational analyses of historical data obtained frommultiple beats or from multiple observation periods. Examples of historytype triggers/markers include, by way of example, but not by way oflimitation, a percentage of abnormal beats detected during anobservation period, a percentage of premature or ectopic beats detectedduring an observation period, heart rate variability during anobservation period (e.g., 5 minutes), and the presence of alternans(i.e., cyclic variations over a plurality of cardiac cycles, e.g.,T-wave alternans or QRS alternans) during an observation period. Otherexamples of history type triggers/markers include, by way of example,but not by way of limitation, short-term and long-term averages of thebeat-to-beat data, trends in such averages (e.g., increasing ordecreasing ST elevation, increasing density of abnormal or prematurebeats, etc.), and trends or periodicities in sensor values obtained fromsensing module 320.

[0057] In one embodiment, detection processing module 405 outputs a setof detection values, denoted D₁, D₂, D₃, . . . , D_(N), corresponding tothe N members in list 410 and where each detection value, D_(i),corresponds to a single member of list 410. In one embodiment, adetection value D_(i) is set to 0 if the corresponding trigger/markerwas not detected during the most recent observation period, and is setto 1 if the trigger/marker was detected during the most recentobservation period.

[0058] Timing of Arrhythmia Predictions and Trigger/Marker Detection

[0059] Not all trigger/marker detections are updated on a beat-to-beatbasis because some triggers/markers (e.g., history typetriggers/markers) generally require information obtained from multiplebeats. In one embodiment, the detection of triggers/markers is performedasynchronously from arrhythmia predictions. Several techniques can beused for this purpose. In one technique, each trigger/marker detectionvalue corresponds to a predetermined observation period during whichtime its value (e.g., present, or not present) is determined. For manytriggers/markers, the appropriate observation period is equal to apredetermined basic time period (BTP). For instance, if the basic timeperiod is 2 minutes, then device 105 tests for the occurrence of suchtriggers/markers in the last 2 minutes. However, detection observationperiods are not necessarily the same for each trigger/marker. In oneembodiment, for example, a heart rate variability trigger/marker isbased on an observation period of approximately 5 minutes.

[0060]FIG. 5 is a timing diagram illustrating generally, by way ofexample, but not by way of limitation, a heart rate variabilitytrigger/marker, D₁, which is updated using a 5 minute observationperiod. In this example, D₁=1 during time periods 500 when heart ratevariability is below its detection threshold and D₁=0 during other timeperiods 505 when heart rate variability is above its detectionthreshold. FIG. 5 also depicts a “long-short sequence” trigger, D₂,which is updated at 2 minute intervals. In this example, D₂=1 duringtime periods 510 when the long-short sequence has been detected in thepreceding 2 minute observation period, and D₂=0 at other times. In thisway, particular trigger/marker detection values are updatedindependently of other trigger/marker detection values in the set oftrigger/marker detection values, D, as sufficient data for making theparticular trigger/marker detection becomes available.

[0061] In one embodiment, predictions of future arrhythmias are made atapproximately regular time intervals 415, such as at the BTP. In oneembodiment, the BTP is based on a fixed number of beats (e.g., 60beats). In another embodiment, the BTP is based on the number of beatsclosest to a fixed duration (e.g., the number of beats just exceeding 1minute, such that the BTP remains approximately constant. In a furtherembodiment, the time duration of the BTP is adjusted over a period oftime during which device 105 is being used, such that predictions offuture arrhythmias are made more or less frequently at times when thepredicted probability for arrhythmias is higher or lower, respectively.

[0062] Other Techniques for Timing Predictions of Arrhythmias

[0063] According to one aspect of the present arrhythmia prediction andprevention techniques, the prediction provides a good match between thetime period covered by the prediction and the requisite times for one ormore potential preventive therapies. For example, a prediction of a99.9% chance that the patient will have an arrhythmia within the next100 years is highly accurate but worthless because the prediction doesnot relate to when the prevention therapy should be delivered or whichprevention therapy should be provided. Similarly, a prediction of a 50%chance that the patient will have an arrhythmia within the next 2 yearsis of limited value because it relates only to long-term preventiontherapies such as selecting patients for device implant or for long-termdrug or pacing therapy. By contrast, a prediction of a 50% chance thatthe patient will have an arrhythmia within the next 30 seconds providesgreat value in an arrhythmia prediction and prevention scheme in animplanted device provided that the decision to invoke a preventivetherapy and the requisite time of action for that therapy can be carriedout quickly enough to significantly reduce the chance of the futurearrhythmia. In this example, the value of the prediction would be quitelimited if the available preventive therapy required, for example, 20minutes to take effect.

[0064] According to another aspect of the present arrhythmia predictionand prevention techniques, the prediction frequency adequately relatesto the time period for which the prediction is valid. For example, itmakes little sense for a prediction covering 1 month to be made onceevery 5 seconds or for a prediction covering 5 seconds to be made onceevery month. Thus, the present arrhythmia prediction and preventiontechniques balance between the time period covered by a prediction, thefrequency with which it is made, and the time required for thepreventive therapy to take effect.

[0065] In one embodiment, device 105 does not necessarily make the sameset of predictions each time predictions are made. One method to do sofor the device to make one set predictions for each BTP that cover timeson the order of the BTP. Then at BTP multiples that correspond to longerintervals (e.g. 20 minutes, 1 hour, 1 day, 1 week, 1 month), acorresponding sets of additional predictions are made which cover thecorrespondingly longer time intervals. The scheduling of thepredictions, their corresponding time intervals are selected, accordingto the above argument, according to the times of action for theprevention therapies available to the device 105.

[0066] Arrhythmia Prediction Example

[0067]FIG. 6 is a block diagram illustrating generally, by way ofexample, but not by way of limitation, one conceptual embodiment ofportions of arrhythmia prediction module 350. In one embodiment,arrhythmia prediction module 350 includes an arrhythmia probabilitycalculation module 600, a conditional probability data bank such as alist of conditional probabilities 605, trigger/marker use data bank suchas trigger/marker use list 610. In a further embodiment, arrhythmiaprediction module 350 also includes an adaptive processing module 615.In one embodiment, conditional probability list 605 and trigger/markeruse list 610 each have members corresponding to the members oftrigger/marker list 410, as explained below.

[0068] In one embodiment, arrhythmia prediction module 350 includes aninput that receives the detection values (D₁, . . . D_(N)) provided bytrigger/marker module 345. In a further embodiment, arrhythmiaprediction module 350 also includes input(s) that receive one or more“arrhythmia detected” signals provided by conventional arrhythmiadetection modules of device 105 (e.g., signals from bradyarrhythmiacontrol module 335 and tachyarrhythmia control module 340). These“arrhythmia detected” signals indicate, among other things, the presenceor absence of one or more present bradyarrhythmias or tachyarrhythmias.Arrhythmia prediction module 350 outputs an arrhythmia prediction topreventive therapy control module 355, which, in turn, bases delivery ofpreventive therapy on the arrhythmia prediction.

[0069] One aspect of the present arrhythmia prediction techniques treatsthe occurrence of an arrhythmia during a particular time period as arandom event. In this embodiment, an arrhythmia prediction includes aprobability calculation estimating the probability that an arrhythmiawill occur within a specified period after the prediction. An example ofsuch a prediction is a 50% probability that an arrhythmia will occurwithin the next 2 minutes. This method of prediction includes both adegree (e.g., 50%) and a well defined time period during which theprediction is applicable (e.g., 2 minutes), as opposed to merelyindicating that the patient is currently at risk.

[0070] In one embodiment, the arrhythmia prediction output fromarrhythmia prediction module 350 includes a set of one or morearrhythmia probability assertions or statements. Each probabilitystatement includes both a magnitude of the probability and a specifiedfuture time period associated therewith. In one embodiment, eachprobability statement also identifies which trigger, marker, orcombination of trigger(s) and/or marker(s) contributed to its magnitude.In a further embodiment, the time period covered by each probabilitystatement (i.e., the time period over which each probability statementis valid) is determined by, among other things, the scheduled predictionfrequency (e.g., predictions made at 1 minute intervals cover a 1 minuteperiod, etc.).

[0071] One conceptual approach to prediction recognizes that the rate atwhich arrhythmia events randomly occur changes relatively slowlycompared to the time period over which predictions are made (i.e., theprediction frequency). Because future arrhythmia rates cannot bemeasured, predictions are based on one or more previous and/or presentestimates of the arrhythmia rate assuming that the arrhythmia ratedoesn't change significantly during the time period over which theprediction is made. The probability that a random arrhythmia event willoccur within the specified prediction time period can be calculatedbased on the present arrhythmia rate. For simplicity, the occurrence ofarrhythmia events can be understood as the result of a Poisson processwith a rate, R. The probability, P, that one or more arrhythmias willoccur within a next time period, T, is P=(1−e^(−RT)) The accuracy of Pdepends on the degree to which the estimate for R is correct, the rate Ris truly constant during the time period T, and whether the underlyingprocess is truly a Poisson random process. While such a conceptualapproach may be helpful to understand the context of certain of thepresent prediction techniques, in one embodiment arrhythmia predictionmodule 305 does not actually formulate an estimate of the underlyingarrhythmia rate. Instead, the arrhythmia rate is reflected in thepresence or absence of the detected triggers/markers, which are used byarrhythmia probability calculation module 600 to formulate a probabilityfor a future arrhythmia, as explained below.

[0072] For example, consider the Ith trigger/marker of trigger/markerlist 410. In one embodiment, the detection value D₁ is either one orzero depending on the respective presence or absence of thistrigger/marker during the basic time period equal to T. Further,consider that the rate of arrhythmias R₁ associated with thistrigger/marker is zero when the trigger/marker is absent and that therate R₁ has a nonzero value when the trigger/marker is present. Thecontribution of the Ith trigger/marker to the arrhythmia probability isD₁ (1−e^(−RT)), or simply D₁×C₁ where CP₁ is the conditional probabilityfor the arrhythmia given that D₁ is present. The total probability foran arrhythmia, P, which includes the contributions from alltrigger/markers, is computed as P=D₁CP₁+D₂×CP₂+D₃×CP₃+. . .+D_(N)×CP_(N), for the case where there is a detection value and aconditional probability for each member of the trigger/marker list 410.

[0073] In one embodiment, the conditional probabilities CP are obtainedempirically from observations made in a patient population. In anotherembodiment, the conditional probabilities CP are seeded withpopulation-based values, but are later adapted to the individual patientbased on empirical observations of that patient. Because the conditionalprobabilities CP are empirically determined, the underlying process neednot be exactly Poisson distributed, and the rate need not be completelyunchanging during the time period T. The empirical estimations for theconditional probabilities CP incorporate deviations from theseassumptions, although their predictive accuracy may be reduced. In oneembodiment, a trigger/marker list 410 and a population-based conditionalprobability list 605 is loaded into device 105 before, during, or afterdevice 105 is implanted in the patient.

[0074] In one embodiment, arrhythmia prediction module 350 also includesa trigger/marker use list 610, which provides a set of usage weights orflags, each usage weight or flag associated with a correspondingtrigger/marker of trigger/marker list 410. According to one aspect ofthe present techniques, it may be desirable to exclude one or more ofthe trigger/marker detections from the total probability computation,such as, as the result of learning patient-specific traits by adaptiveprocessing module 615. In trigger/marker use list 610, each usage weightor flag indicates how that trigger/marker detection value andconditional probability enters into the arrhythmia probabilitycomputation. In one embodiment, usage flags provide a binary indicationof whether or not that particular trigger/marker detection value andconditional probability enters into the arrhythmia probabilitycalculation. In another embodiment, usage weights provide a degree towhich the detection value and conditional probability enters into thearrhythmia probability calculation. In such an embodiment, thearrhythmia probability calculation may be normalized, based on thevalues of the usage weights, such that the arrhythmia probability rangesbetween 0 and 1.

[0075] Determining Triggers/Markers and Conditional Probabilities from aPopulation

[0076] In one embodiment, a relevant patient population is used toobtain initial or actual estimates for conditional probabilities and/orto select particular triggers/markers for active use in the presentprediction techniques. Patient-specific and adaptive techniques aredescribed later. One approach for determining population-basedparameters uses long-term data (e.g., from sensing module 310 and/orsensing module 320) from a representative subset of the clinicallyrelevant population. This long-term data obtained from a plurality ofpatients in the clinically relevant patient population is referred to asthe population database. Data in the population database is divided intotime periods equal to the BTP. After each such time period,trigger/marker detection processing is performed over the entirepopulation database for all triggers/markers. The presence or absence ofeach trigger/marker (i.e., the set of detection values, D) is notedafter each such time period. The presence or absence of one or morearrhythmias is also noted after each such time period. Onepopulation-based estimate for a particular conditional probabilityassociated with the Ith trigger/marker (i.e. CP₁) is simply CP₁=[(totalnumber of time periods with arrhythmias that were preceded by a timeperiod with D₁ present)÷(total number of time periods with D₁ present)].A similar estimate is made for all members in trigger/marker list 410.This embodiment uses time periods equal to the same duration (i.e., theBTP) for all triggers/markers. Alternatively, the population database isdivided into equal-length time periods that are different betweenindividual triggers/markers. For example, for each trigger/marker, thepopulation database may be divided up into equal length time periodsthat correspond approximately to the time period covered by thatparticular trigger/marker.

[0077] Determining Predictive Capability of Particular Triggers/Markers

[0078] Not all possible triggers/markers may have predictive power,either in the clinically relevant patient population, or in theparticular patient toward which the disclosed techniques are applied.The present techniques include a method of selecting those particulartriggers/markers, in the trigger/marker list 410, that have usefulpredictive capability. One such method uses a Chi squared test based onthe population database, but other statistical test methods may also beused. For example, in the Chi squared test, the presence or absence ofarrhythmias and the available triggers/markers in each time period inthe population database yields the following values: #BTP=total numberof BTP's examined, #D⁺=total number of BTP's in which the trigger/markerwas detected, #D⁻=total number of BTP's in which the trigger/marker wasnot detected, #A⁺=total number of BTP's in which arrhythmia wasdetected, #A⁻=total number of BTP's in which arrhythmia was notdetected, # D⁺A⁺=number of BTP with the trigger/marker detected followedby a BTP with arrhythmia, # D⁺A⁻=number of BTP with the trigger/markerdetected followed by a BTP without arrhythmia, # DA⁺=number of BTPwithout the trigger/marker detected followed by BTP with arrhythmia, #D⁻A⁻=number of BTPs without the trigger/marker detected followed by BTPwithout arrhythmia.

[0079] A statistical test determines whether the observed occurrences of#D⁺A⁺, #D⁺A⁻, #D⁻A⁺, and #D⁻A⁻ are different from those that would beexpected if the trigger/marker did not have predictive power. Oneexample such test computes the following sum: $\begin{matrix}{\text{Sum} = \quad {\frac{\left\lbrack {{\# D^{+}A^{+}} - {\# A^{+} \times \# {D^{+}/\#}{BTP}}} \right\rbrack^{2}}{\# A^{+} \times \# {D^{+} \div \#}{BTP}} +}} \\{\quad {\frac{\left\lbrack {{\# D^{+}A^{-}} - {\# A^{-} \times \# {D^{+}/\#}{BTP}}} \right\rbrack^{2}}{{\# A^{-} \times \# D^{+}} \equiv {\# {BTP}}} +}} \\{\quad {\frac{\left\lbrack {{\# D^{-}A^{+}} - {\# A^{+} \times \# {D^{-}/\#}{BTP}}} \right\rbrack^{2}}{\# A^{+} \times \# {D^{-}/\#}{BTP}} +}} \\{\quad \frac{\left\lbrack {{\# D^{-}A^{-}} - {\# A^{-} \times \# {D^{-}/\#}{BTP}}} \right\rbrack^{2}}{{\# A^{-} \times \# D^{-}} \equiv {\# {BTP}}}}\end{matrix}$

[0080] This represents the Chi Square value (1 degree of freedom) thattests if the arrhythmia is associated with the trigger/marker. If thesum exceeds 3.84, for example, we are 95% confident that thetrigger/marker provides predictive capability for the arrhythmia. A morecomplete multi-variate statistical analysis would provide strongerconclusions about the trigger/marker's role when considered togetherwith other triggers/markers having independent predictive power.Strictly speaking, the form for the above probability computation isbased on an approximation, i.e., that the predictive capability of theindividual triggers/markers are independent from each other.

[0081] Patient Specific Adaptive Processing Example

[0082] In one embodiment, device 105 includes a patient specificadaptive processing module 615, as illustrated in FIG. 6. In oneexample, adaptive processing module 615 modifies conditional probabilitylist 605 and trigger/marker use list 610. The present techniquesrecognize that it is unlikely for some set of one or moretriggers/markers to be so powerful at predicting arrhythmias that theyalways worked in all patients. The predictive capability of a particularset of one or more triggers/markers will most likely vary betweendifferent patients. Population-based average values may be suboptimalfor a particular patient. In one embodiment, device 105 examineslong-term data specific to the individual patient in which it isimplanted. Thus, one aspect of the present techniques allow device 105to adapt to an individual patient to improve the accuracy and confidencein arrhythmia predictions over a time during which device 105 is beingused.

[0083] One embodiment extracts patient-specific parameters (e.g.,conditional probabilities and/or weights) analogously to the techniquesdescribed above that extract population-based parameters. In thisembodiment, the members of the trigger/marker list 410 are initiallyestablished based on clinical research in a population of interest, andare loaded into device 105. After implantation of device 105 in aparticular patient, as ongoing data is acquired from the patient,adaptive processing module 615 manages trigger/marker use list 610 bydetermining which members of trigger/marker list 410 provide predictivecapability for arrhythmia prediction in the particular patient.

[0084] One such technique initially sets the members of thetrigger/marker use list 610 to “do not use.” At some point afterimplant, adaptive processing module 615 examines each BTP to detect thepresence or absence of (1) arrhythmias and (2) the triggers/markers intrigger/marker list 410 to form a patient-specific database. Device 105recurrently performs the above Chi squared test to determine (to withina predetermined level of confidence) if each member of trigger/markerlist 410 provides predictive capability in the particular patient. If amember does provide such predictive capability, the usage value for thatmember is set to “use” in trigger/marker use list 610. Such a techniqueonly allows an arrhythmia prediction based on a particulartrigger/marker after sufficient confidence exists that its predictivecapability in the particular patient is sufficiently high.

[0085] Another such technique initially sets the members of thetrigger/marker use list 610 to “use,” such as where population baseddata indicates the likelihood that these members of the trigger/markeruse list are likely to have sufficient predictive capability. In thisembodiment, the corresponding usage values are set to “do not use” ifthe patient-specific database demonstrates that the trigger/marker doesnot provide adequate predictive capability in the particular patientdespite its good performance in the population. Such a technique stopsusing the trigger/marker to make predictions after sufficient confidenceexists that its predictive capability in the particular patient isinadequate.

[0086] For patient-specific adaptive processing, the ratios (R⁺ _(P)=#D⁺A⁺÷#D⁺) and (R⁻ _(P)=# D⁻A⁺÷# D⁻) from the population database (hencethe P subscripts) are used to compute the following sum: $\begin{matrix}{\text{Sum} = \quad {\frac{\left\lbrack {{\# D^{+}A^{+}} - {R_{p}^{+} \times \# D^{+}}} \right\rbrack^{2}}{R_{p}^{+} \times \# D^{+}} +}} \\{\quad {\frac{\left\lbrack {{\# D^{+}A^{-}} - {\left( {1 - R_{p}^{+}} \right) \times \# D^{+}}} \right\rbrack^{2}}{{\left( {1 - R_{p}^{+}} \right) \cdot \#}D^{+}} +}} \\{\quad {\frac{\left\lbrack {{\# D^{-}A^{+}} - {R_{p}^{-} \times \# D^{-}}} \right\rbrack^{2}}{R_{p}^{-} \times \# D^{-}} +}} \\{\quad \frac{\left\lbrack {{\# D^{-}A^{-}} - {\left( {1 - R_{p}^{-}} \right) \times \# D^{-}}} \right\rbrack^{2}}{\left( {1 - R_{p}^{-}} \right) \times \# D^{-}}}\end{matrix}$

[0087] where the values of # D⁺, # D⁺A⁺, etc. are obtained from thepatient specific database. This sum, which is a Chi squared value, iscompared to another predetermined threshold value. If the sum exceedsthe threshold, then that trigger/marker does not predict the arrhythmiain the specific patient in the same manner as it does in the population.This could occur either if the trigger/marker provides lesser predictivepower than in the population, or also if the trigger/marker providesgreater predictive power than in the population. Consequently, thecorresponding usage flag in trigger/marker use list 610 is only set to“do not use” if the patient-specific test also demonstrates that thetrigger/marker does not provide predictive power in the particularpatient. In one embodiment, these threshold values (population-basedand/or patient-specific) are programmable. In a further embodiment,these threshold values (population-based or patient-specific) areprogrammed to either the same value, or to different values.

[0088] The predictive capability of a particular trigger/marker couldchange over time in a particular patient. In one embodiment, adaptiveprocessing module 615 recurrently sets and/or resets the usage values inthe trigger/marker use list 610 over time while device 105 is beingused. In this embodiment, testing for patient-specific predictivecapability of each trigger/marker is carried out recurrently. If aparticular trigger/marker is not presently in use, then the test result(i.e., the sum described above) would be required to exceed a firstthreshold value to change the usage value of the trigger/marker to“use.” If the trigger/marker is presently in use, then the test result(i.e., the sum described above) would be required to exceed a secondthreshold value to change the usage value of the trigger/marker to “donot use.” These threshold values can either be the same, oralternatively can be different to provide hysteresis that would reducethe number of spurious transitions.

[0089] In one embodiment, adaptive processing module 615 also providesand adaptively modifies conditional probabilities in conditionalprobability list 605 based on observations in the specific patient. Fora particular patient, a trigger/marker's conditional probability mayvary from the population-based value. Conditional probability list 605is initially seeded with population-based conditional probabilities.However, as device 105 acquires data from the particular patient,patient-specific conditional probabilities are used, as described below.

[0090] In one embodiment, adaptive processing module 615 implements thepatient-specific conditional probabilities by recurrently estimating theconditional probability for each trigger/marker analogously to theirpopulation-based estimation described above. However, patient-specificdata is used rather than population-based data. For each member of thetrigger/marker list 410, device 105 estimates the patient-specificconditional probability as CP_(1 est)=# D₁ ⁺A⁺÷# D₁ ⁺, where # D₁ ⁺ isnumber of BTP's in which the Ith trigger/marker was detected, and # D₁⁺A⁺is the number of BTP in which (1) trigger/marker I was detected, and(2) the BTP was followed by a BTP with an arrhythmia.

[0091] Such recurrent reforecasting of conditional probabilities alsoincludes a second step of determining when the patient-specific valuesshould be used rather than the population-based values. Counters storing# D₁ ⁺A⁺ and # D₁ ⁺ are initially reset to zero. Whenever a BTPcontaining trigger/marker I is detected, device 105 formulates a newestimate for the conditional probability CP_(1 est) by incrementing # D₁⁺ and # D₁ ⁺A⁺ if the following BTP includes an arrhythmia.

[0092] If the initial value for CP₁ is also correct for the particularpatient, then after observing # D₁ ⁺ BTP's with trigger/marker I, onewould expect (# D₁ ⁺×CP₁) cases where an arrhythmia was found. More orfewer arrhythmias may result from chance alone. The standard deviationfor the number of expected arrhythmias is {# D₁ ⁺×CP₁ (1−CP₁)}^(½). A95% confidence interval would include about 1.96 times this standarddeviation above and below the expected values. Thus, if the initiallyentered values for CP₁ were also valid for this particular patient, thenthere exists a 95% confidence that the number of arrhythmias observedafter # D₁ ⁺ occurrences of trigger/markerIis between [# D₁ ⁺×CP₁−1.96{# D₁ ⁺×CP₁×(1−CP₁)}^(½)] and [# D₁ ⁺×CP₁+1.96 {# D₁⁺×CP₁×(1−CP₁)}^(½)]. Expressed as percentages of # D₁ ⁺ this yields thefollowing confidence interval for the conditional probability:[CP₁−1.96×{CP₁×(1−CP₁)/# D₁ ⁺}^(½)] and [CP₁ ⁺1.96×{CP₁×(1−CP₁)/# D₁⁺}^(½)].

[0093] If the estimate for the conditional probability CP_(1 est) fallsoutside this range, there exists a 95% confidence that CP_(1 est) is abetter estimate for this particular patient than CP₁. In that case, theestimated value for the conditional probability is used for subsequentpredictions of future arrhythmias rather than the population-basedvalues. Because the patient's conditional probabilities may change overtime, the same procedure can be repeated recurrently by using the newconditional probability as the given value and forming another newestimate based on a new set of observations. In this way, theconditional probabilities are recurrently updated to more appropriatevalues as device 105 is being used.

[0094] One alternate method of updating conditional probabilitiesrecurrently updates the patient-specific conditional probability foreach trigger/marker in such a way that the values eventually approach ortrack the true values for the specific patient. In this method, theconditional probabilities are seeded with population-based values. Then,each time a BTP containing one of the triggers/markers is observed,device 105 notes the presence or absence of an arrhythmia during thenext BTP. If an arrhythmia is present, then the conditional probabilityfor that trigger/marker is increased by a small step (e.g.,CP₁=CP₁+STEP*(1−CP₁)). If the arrhythmia is not present, then theconditional probability for that trigger/marker is decreased by a smallstep, as illustrated below (e.g., CP₁=CP₁−STEP*(CP₁)).

[0095] If the patient's true conditional probability equals CP,, thenthe ratio of BTP's with arrhythmias absent to those with arrhythmiaspresent should equal (1−CP₁)/CP₁. For a conditional probability of 20%,for example, an average of 4 BTPs without arrhythmias are expected foreach BTP with an arrhythmia. In this case, the conditional probabilityis decreased 4 times by a small amount (0.2× STEP) but increased 1 timeby a large amount (0.8× STEP), yielding no net drift in the CP, overtime. If the patient's actual conditional probability was significantlyhigher than CP₁, then there would be a higher proportion of BTP's withan arrhythmia. As a result, CP₁ would tend to increase until CP₁approached the correct value. Similarly, if the actual value was toolow, CP₁ would decrease until it approached the correct value. In oneembodiment, the selection of STEP is small (0.05) such that thesechanges are stable over time.

[0096] Examples of Alternate Methods for Arrhythmia Prediction

[0097] In one alternate embodiment, arrhythmia prediction module 350incorporates into an arrhythmia prediction the number of times atrigger/marker occurs during a BTP or other time period. An arrhythmiamay occur each time a particular trigger/marker occurs. Repeatedappearances of a trigger/marker may suggest a stronger relativepredictive value. Consequently, multiple occurrences of a trigger/markerprovide a higher arrhythmia probability. In this embodiment, each memberin the conditional probability list 605 includes a set of one or moreconditional probability values. In one example, the first value in theset reflects the conditional probability when the correspondingtrigger/marker occurred once during the basic time period, the secondset reflects the conditional probability when the correspondingtrigger/marker occurred twice during the basic time period, etc In thisembodiment, the set of conditional probabilities may alternativelyreflect ranges of the number of trigger/marker detections (e.g., between3 and 5 detections, fewer that 8 detections, more than 3 detections,etc.). The population-based and patient-specific values for these setsof conditional probabilities are also obtained by extension of thetechniques described above. The patient-specific adaptive processing forthese sets of conditional probabilities is also obtained by extension ofthe techniques described above.

[0098] This alternate embodiment also provides a different technique oftrigger/marker detection. Instead of setting the detection value, D₁, toeither zero or one, the trigger/marker detection processing module 405sets the detection value D₁ to the number of times the Ithtrigger/marker occurred during the BTP. The arrhythmia probabilitycalculation is computed as P=D₁×P_(1,D1)+D₂×CP_(2,D2)+D₃×CP_(3,D3)+. . .+D_(N)×CP_(N,DN), where CP_(I,K) is the conditional probability whentrigger/marker I is present K times during the BTP, and N is the numberof triggers/markers.

[0099] A second alternate embodiment recognizes that if two (or more)different triggers/markers occur during the same BTP, the probabilityfor arrhythmias may be different from the linear sum of the conditionalprobabilities when the trigger/marker occurred alone (i.e., without theother one or more triggers/markers). In this embodiment, detectionprocessing module 405 outputs binary detection values for the members inthe trigger/marker list 410. However, in this embodiment, arrhythmiaprediction module 350 forms a number from 0 (none of triggers/markers intrigger/marker list 410 detected) to 2^(N)−1 (all of thetriggers/markers in trigger/marker list 410 detected) indicating whichof the triggers/markers in trigger/marker list 410 were detected duringthe BTP, where N is the number of members in trigger/marker list 410. Inthis embodiment, the members of conditional probability list 605 andtrigger/marker use list 610 do not correspond to the members oftrigger/marker list 410. Instead, the members of conditional probabilitylist 605 and trigger/marker use list 610 correspond to the variouscombinations of the 2^(N)−1 possible combinations of triggers/markersthat could be detected. The arrhythmia probability calculation iscomputed as P=CP_(K), where K is a number (from 0 to 2^(N)−1) thatreflects the current combination of detected triggers/markers (i.e.,those triggers/markers having a detection value of 1).

[0100] Automated Preventive Therapy Selection Processing Example

[0101] Preventive therapy control module 355 automatically decideswhether to invoke preventive therapy based on arrhythmia probabilitystatements obtained from arrhythmia prediction module 350. Preventivetherapy control module 355 provides output signals to therapy module 315to deliver pacing and/or shock and/or other arrhythmia preventiontherapies.

[0102]FIG. 7 is a block diagram illustrating generally, by way ofexample, but not by way of limitation, one conceptual embodiment ofportions of preventive therapy control module 355. In this embodiment,prediction scheduler 700 schedules predictions of future arrhythmias.Preventive therapy decision module 705 decides whether arrhythmiaprevention therapy is warranted. Preventive therapy selection module 710selects one or more appropriate prevention therapies. Preventionactivation module 715 activates the selected arrhythmia preventiontherapy. Preventive therapy control module 355 also includes aprevention therapy list 720, and a trigger/marker vs. preventive therapytranslation matrix 725 that relates the prevention therapies ofpreventive therapy list 720 to the triggers/markers used by arrhythmiaprediction module 350 in predicting future arrhythmias. The varioussubmodules in therapy control module 355 are illustrated as such forconceptual purposes only; alternatively, these submodules may beunderstood as being incorporated in arrhythmia prediction module 350 orelsewhere.

[0103] In one embodiment, prevention therapy list 720 includes all thepossible arrhythmia prevention therapies that device 105 can deliver tothe patient. List 720 is programmed into device 105 either in hardware,firmware, or software. The members of list 720 may be selected based, byway of example, but not by way of limitation on: the patient's lead andelectrode configuration, the patient's dependence on pacing,functionality of the patient's AV node, etc. In one embodiment, eachmember of preventive therapy list 720 includes information fields (e.g.,pacing rate, interval sequences, etc.) that establish characteristics ofthat particular arrhythmia prevention therapy. In one embodiment,preventive therapy list 720 includes immediate, short-term,intermediate-term, and/or long term preventive therapies.

[0104] Immediate preventive therapies include, by way of example, butnot by way of limitation: overdrive atrial pacing, such as for patientswith functional AV nodes; demand atrial pacing, such as for patientswith functional AV nodes; overdrive ventricular pacing; demandventricular pacing; simultaneous or sequenced overdrive atrial andventricular pacing; simultaneous or sequenced demand right ventricularand left ventricular overdrive pacing, such as for patients with a leftventricular pacing electrode; simultaneous or sequential rightventricular and left ventricular demand pacing, such as for patientswith a left ventricular pacing electrode.

[0105] Short-term preventive therapies include, by way of example, butnot by way of limitation: repetitive overdrive atrial pacing, such asfor patients with functional AV nodes; repetitive simultaneous orsequenced demand atrial and ventricular pacing; subthreshold cardiacstimulation, such as using defibrillation lead electrodes; neuralstimulation of the autonomic nervous system, such as for patients withautonomic stimulation leads; global pacing pulses implemented byproviding low energy shocks via defibrillation electrodes; delivery ofcertain drugs.

[0106] Intermediate-term preventive therapies include, by way ofexample, but not by way of limitation, an alert/warning for the patientand/or physician such as by providing an audible tone or other signal;delivery of certain drugs.

[0107] Long-term preventive therapies include, by way of example, butnot by way of limitation, diagnostic warnings for the physician, such asby transmitting diagnostic information from device 105 to externalprogrammer 125 using a telemetry or other communication link; deliveryof certain drugs.

[0108] According to one aspect of the present system, each member ofpreventive therapy list 720 is associated with a required time ofaction, which includes one or more of a time for the therapy to becomeeffective and/or a time after which the therapy is no longer effective.Accordingly, in one embodiment, the prediction scheduler 700 considersonly those members of the preventive therapy list that can be expectedto be effective within a time frame commensurate with the predictiontime period. In other embodiments, information relating to the timeframe in which a particular therapy is expected to be effective isincorporated into the translation matrix 725, and the prediction timeperiod is added to the trigger/marker list 410. Other physiologicalvariables can also be added to the list 410 to form a kind ofphysiological state vector that can be mapped to a particular member ofthe preventive therapy list 720, with the translation matrixincorporating information relating to the appropriateness each member ofthe therapy list for a given value of a physiological variable.

[0109] In one embodiment, only one member of the preventive therapy list720 is invoked at any particular time. Combinations of differentpreventive therapies are also provided, but each such combination istreated as a separate entry in preventive therapy list 720. For example,“initiate overdrive ventricular pacing” is one member in preventivetherapy list 720 while “initiate an alert/warning stimulus” is adifferent member in preventive therapy list 720. A combined therapyusing both “initiate overdrive ventricular pacing” and “initiate analert/warning stimulus” is treated as yet another entry in preventivetherapy list 720. This simplifies the tasks of preventive therapyselection module 710 and prevention activation module 715.

[0110] Preventive therapy selection module 710 selects an arrhythmiaprevention therapy based on outputs from preventive therapy decisionmodule 705. If preventive therapy decision module 705 determines thatthe degree and confidence in the arrhythmia prediction warrant somepreventive therapy, as discussed above, then preventive therapyselection module 710 selects a member of the preventive therapy list 720to be invoked. In one embodiment, the selection of a prevention therapyis based on the set of trigger/marker detection values, D, upon whichpreventive therapy decision module 705 based the decision to providearrhythmia prevention therapy. Translation matrix 725 translates betweenthe trigger/marker detection values D and selection of the appropriatearrhythmia prevention therapy from preventive therapy list 720.

[0111]FIG. 8 is a diagram illustrating generally, by way of example, butnot by way of limitation, one embodiment of a translation matrix 725. Inthis embodiment, rows of translation matrix 725 correspond to themembers of trigger/marker list 410. Columns of translation matrix 725correspond to the members of preventive therapy list 720. In oneembodiment, the cells in translation matrix 725 are set to either “Use,”“Do Not Use,” or “Don't Care.” A “Use” entry indicates that thecorresponding preventive therapy is expected to provide a beneficialeffect to reduce the risk of the arrhythmia when the associatedtrigger/marker is present. A “Do Not Use” entry indicates that thecorresponding preventive therapy is expected to result in a detrimentaleffect that may increase the risk of arrhythmia when the associatedtrigger/marker is present. The “Don't Care,” entry indicates that thecorresponding preventive therapy is expected to neither increase ordecrease the risk of arrhythmia when the associated trigger/marker ispresent.

[0112] For the particular example illustrated in FIG. 8, when the firstmember of trigger/marker list 410 is present, preventive therapies #1and #6 are likely to be useful, but preventive therapy #4 should not beused because it is likely to be detrimental. The other possiblepreventive therapies are deemed to have no effect when trigger/marker #1is present.

[0113] In one embodiment, the values in translation matrix 725 arepopulation-based values that are initially loaded into device 105. Thesepopulation-based values are obtained for the population of interestbased on observations of: the occurrences of arrhythmias; the presenceof preceding triggers/markers; and the estimated or empirically derivedeffect of the particular preventive therapies on decreasing theprobability of arrhythmia.

[0114] In one embodiment, when preventive therapy decision module 705indicates that a preventive therapy is needed, there will be some set ofdetected triggers/markers that initiated the decision to invokearrhythmia prevention therapy. Therapy selection module 710 considersthose rows in translation matrix 725 that correspond to those particulartriggers/markers that initiated arrhythmia prevention therapy. Selectionof the appropriate preventive therapy is based on a score determined foreach of the preventive therapies in preventive therapy list 720. Theparticular preventive therapy with the highest score is selected bypreventive therapy selection module 710.

[0115] In one embodiment, each member of preventive therapy list 720 isconsidered. For those rows that correspond to the presently detectedtriggers/markers, the number of “Use” entries is added to obtain thescore. If any “Do Not Use” entry is present, however, the score is setto zero. In this way, each of the preventive therapies has a scorebetween zero and the number of detected triggers/markers having acorresponding “Use” entry. Therapy selection module 710 selects thatpreventive therapy with the highest score. Where there is a tie, thepreventive therapy appearing earliest in preventive therapy list 720 isused. Where all possible preventive therapies have a score of zero, thennone of the available preventive therapies are deemed appropriate, andno preventive therapy is invoked. In one alternate embodiment, the scoreis also based on the conditional probability for each detectedtrigger/marker. In another alternate embodiment, a threshold value isincluded, such that the highest scoring preventive therapy must have ascore that reaches (or exceeds) the threshold value before anypreventive therapy is selected.

[0116] Prevention activation module 715 uses the information containedin the preventive therapy list 720 and/or translation matrix 725 foractivating delivery of the selected therapy by therapy module 315.Device 105 delivers the activated therapy for a predetermined amount oftime, which is also included in the preventive therapy list 720. Thearrhythmia prevention therapy is delivered, by way of example, but notby way of limitation, by controlling the pacing and/or shock circuits orby storing information for later review (via telemetry or othercommunication to external programmer 125) by the physician or otheruser.

[0117] Example of Scheduling Predictions

[0118] Prediction scheduler 700 schedules the frequency with whicharrhythmia predictions are made. As discussed earlier, the BTP used todetect a trigger/marker for prediction may differ from the time periodcovered by a particular prediction which, in turn, may differ from thetime required for the preventive therapy to have an effect. Predictionscheduler 700 should schedule predictions in a rational manner, forallowing efficient operation.

[0119] As described above, the arrhythmia probability statements can beconceptualized as having the following basic form: “In the next Y timeperiod there is an X percent chance of arrhythmia due to the combinationof triggers/markers D.” In one embodiment, predictions are made at amaximum of 2 minute intervals, and the BTP for detectingtriggers/markers is also 2 minutes. In an alternate embodiment, however,the prediction frequency may be different from the BTP frequency.Intervals between predictions may be longer or shorter than the BTP, andmay be variable or fixed. In one embodiment, the time period covered bythe predictions (i.e., the values for Y) are either 2 minutes (i.e., 1BTP), 30 minutes (i.e., 15 BTPS), 1 day, 1 week, or 1 month. Predictionscovering a 2 minute period are made at 2 minute intervals. Predictionscovering a 30 minute period are made at 10 minute intervals. Predictionscovering a 1 day period are made at 6 hour intervals. Predictionscovering a 1 week period are made daily. Predictions covering a 1 monthperiod are made weekly. Thus, in this embodiment, the predictionscovering shorter time periods are made at a frequency corresponding tothe time period covered by the prediction. Predictions covering longertime periods are made more often than the time period covered by theprediction. The timing of the predictions relative to the time coveredby the prediction could be made in many other ways without departingfrom the techniques disclosed in this document.

[0120] Preventive Therapy Decision

[0121] Preventive therapy decision module 705 decides whether preventivetherapy is warranted. In one embodiment, this task is performed at each2 minute interval (i.e., every BTP), at which time it considers allarrhythmia predictions existing at that time. In one embodiment, thetotal probability for arrhythmia for each of the prediction time periodsis compared to corresponding threshold values.

[0122] If the arrhythmia probabilities during all of covered timeperiods fail to exceed the corresponding threshold values, then nosignificant arrhythmia risk is deemed to exist and no preventive therapyis provided. If the arrhythmia probability during any of the coveredtime periods exceed the corresponding threshold value, then asignificant arrhythmia risk is deemed to exist, and the arrhythmiaprevention therapy is selected and activated as described above.

[0123] Conclusion

[0124] Although the above description described particular embodimentsusing an implanted cardiac rhythm management device includingdefibrillation capability, the techniques can also be used in othercardiac rhythm management systems including, without limitation,implanted or external pacemakers, or other acute or chronic cardiac careor monitoring devices. Moreover, although the techniques were describedusing those electrodes and sensors available in implanted cardiac rhythmmanagement devices, different and/or additional sensing and/orstimulation electrodes may be used (e.g., for sensing or stimulatingsympathetic or parasympathetic nerves or ganglion, or for sensing pH,pO₂ or K+ concentration in blood, etc.).

[0125] It is to be understood that the above description is intended tobe illustrative, and not restrictive. Many other embodiments will beapparent to those of skill in the art upon reviewing the abovedescription. The scope of the invention should, therefore, be determinedwith reference to the appended claims, along with the full scope ofequivalents to which such claims are entitled.

What is claimed is:
 1. A method, comprising: detecting a conditioningevent statistically associated with the occurrence of an arrhythmia in apatient's heart; and, predicting the occurrence of an arrhythmia withina specified prediction time period if an estimated arrhythmiaprobability exceeds a specified threshold value, wherein the estimatedarrhythmia probability is computed from a conditional arrhythmiaprobability associated with the conditioning event that is derived frompast observations of instances in which the conditioning event occursalone or together with an arrhythmia within a specified time period. 2.The method of claim 1 wherein the conditioning event is a marker eventcorresponding to detection of a physiological state statisticallyassociated with occurrence of cardiac anythmias.
 3. The method of claim1 wherein the conditioning event is a trigger event that increases therisk of an arrhythmia occurring via a depolarization that serves as asource for the arrhythmia.
 4. The method of claim 1 wherein theconditioning event is a specific morphology of a waveform representingelectrical activity of the heart.
 5. The method of claim 1 wherein theconditioning event is a specific pattern of activation times ofdifferent areas of the heart as sensed by a plurality of electrodes. 6.The method of claim 1 wherein the conditioning event is a specificsequence pattern of heartbeats with respect to time.
 7. The method ofclaim 1 wherein the conditioning event is a value of a measuredphysiological variable.
 8. The method of claim 1 wherein theconditioning event is a statistic based upon a history of occurrences ofconditioning events.
 9. The method of claim 1 wherein the conditionalarrhythmia probability CP is a ratio of the number of observed instancesin which the conditioning event is followed by an arrhythmia within aspecified basic time period to the total number of observed instances ofthe conditioning event.
 10. The method of claim 9 further comprisingestimating a rate C at which the conditioning event occurs, and furtherwherein the estimated arrhythmia probability is calculated by theexpression: estimated arrhythmia probability=CP×(1−e ^(−CT)).
 11. Themethod of claim 1 wherein the conditional arrhythmia probability iscalculated by the expression: 1−e^(−RT) where T is a measure of thespecified prediction time period, and R is an estimate of the rate atwhich arrhythmias occur while the conditioning event is present.
 12. Themethod of claim 11 wherein the rate R is a ratio of: 1) the number ofinstances in which the conditioning event is followed by an arrhythmiawithin a specified basic time period, to 2) the length of the basic timeperiod multiplied by the total number of basic time periods in which theconditioning event is observed.
 13. The method of claim 12 furthercomprising estimating a rate C at which the conditioning event occurs,and further wherein the estimated arrhythmia probability is calculatedby the expression: (1−e ^(−RT))(1−e ^(−CT)).
 14. The method of claim 1further comprising assigning a culpability to a conditioning event forcausing an observed instance of an arrhythmia when a specified criterionis met, and further wherein the conditional arrhythmia probability CP isa ratio of the number of observed instances in which the conditioningevent was deemed culpable for causing an arrhythmia, to the total numberof observed occurrences of the conditioning event.
 15. The method ofclaim 14 further comprising estimating a rate C at which theconditioning event occurs, and further wherein the estimated arrhythmiaprobability is calculated by the expression: estimated arrhythmiaprobability=CP×(1−e ^(−CT)).
 16. The method of claim 14 wherein theculpability criterion is the proximity in time between the conditioningevent and the onset of an arrhythmia.
 17. The method of claim 14 whereinthe culpability criterion is the magnitude of the detected conditioningevent prior to the onset of an arrhythmia.
 18. The method of claim 14wherein the culpability criterion is the frequency of occurrence of theconditioning event within a specific time period prior to the onset ofan arrhythmia.
 19. The method of claim 1 wherein a plurality ofconditioning events statistically associated with the occurrence of anarrhythmia are detected and further wherein a composite estimatedarrhythmia probability is compared with the threshold value in order topredict the occurrence of an arrhythmia, the composite arrhythmiaprobability being a combination of the estimated arrhythmiaprobabilities associated with each detected conditioning event.
 20. Themethod of claim 19 wherein the composite arrhythmia probability iscalculated by adding the estimated arrhythmia probabilities associatedwith each detected conditioning event.
 21. The method of claim 20wherein the estimated arrhythmia probabilities associated with eachdetected conditioning event are calculated by multiplying theconditional arrhythmia probability derived for a conditioning event bythe number of detected occurrences of the event within a specified timeperiod.
 22. The method of claim 19 wherein each conditional arrhythmiaprobability is a ratio of the number of instances in which theconditioning event is observed to be followed by an arrhythmia within aspecified basic time period to the total number of observed instances ofthe conditioning event.
 23. The method of claim 22 wherein theconditional arrhythmia probabilities are derived using different basictime periods.
 24. The method of claim 19 wherein the conditionalarrhythmia probabilities are based upon past observations of theoccurrences of events and arrhythmias taken from population data. 25.The method of claim 19 wherein the conditional arrhythmia probabilitiesare based upon past observations of the occurrences of events andarrhythmias taken in real-time from a particular patient.
 26. The methodof claim 25 wherein the conditional arrhythmia probabilities are basedinitially upon past observations of the occurrences of events andarrhythmias taken from population data and each probability issubsequently updated from a previous value to a present value withobservations taken in real-time from a particular patient.
 27. Themethod of claim 25 further comprising periodically updating aconditional arrhythmia probability from a previous value to a presentvalue with observations taken in real-time from a particular patient 28.The method of claim 27 wherein a conditional arrhythmia probability isupdated only if the present value differs by a predetermined amount fromthe previous value.
 29. The method of claim 28 further comprisingtesting the amount by which the present value differs from the previousvalue for statistical significance before a conditional arrhythmiaprobability is updated.
 30. The method of claim 25 further comprisingtesting the statistical association of a conditional arrhythmiaprobability with real-time observation data.
 31. The method of claim 30further comprising discontinuing use of a conditional arrhythmiaprobability in deriving a composite conditional probability if thestatistical association is below a specified value.
 32. The method ofclaim 25 further comprising incrementing or decrementing a conditionalarrhythmia probability by a specific amount after a prediction timeperiod in accordance with whether the arrhythmia occurred or not,respectively.
 33. The method of claim 1 further comprising delivering apreventive arrhythmia therapy if the estimated arrhythmia probabilityexceeds a exceeds a therapy threshold.
 34. The method of claim 33further comprising selecting a particular therapy to be delivered from agroup of one or more available therapy modalities.
 35. The method ofclaim 34 wherein the group of available therapy modalities includesdelivery of one or more of a plurality of pharmacological agents. 36.The method of claim 34 wherein the group of available therapy modalitiesincludes modalities selected from a group consisting of cardiac pacingin a selected pacing mode, delivery of cardioversion/defibrillationshocks, and neural stimulation.
 37. The method of claim 34 wherein thegroup of available therapy modalities includes issuance of a warningsignal.
 38. The method of claim 34 wherein the selection of a particulartherapy to be delivered is based upon ascertaining a physiologic stateof the patient and deciding whether or not a particular availablemodality of therapy is appropriate for delivery to the patient in thatphysiologic state.
 39. The method of claim 38 wherein the physiologicstate ascertained for selecting a therapy modality includes theprediction time period of the estimated arrhythmia probability.
 40. Themethod of claim 38 wherein the physiologic state ascertained forselecting a therapy mode includes the particular conditioning eventsused to make the arrhythmia prediction.
 41. The method of claim 38wherein the physiologic state ascertained for selecting a therapy modeincludes the presence or not of specific conditioning events within aspecified prior time period.
 42. The method of claim 38 wherein thephysiologic state ascertained for selecting a therapy mode includes themagnitude of a measured physiologic variable.
 43. The method of claim 38wherein the decision as to whether or not to deliver a particular modeof preventive therapy takes the form of a matrix mapping of a statevector representing a specific physiologic state to a specific therapyor therapies considered most appropriate for preventing an arrhythmia inthat physiologic state, wherein the matrix mapping is performed using atranslation matrix containing information relating to theappropriateness of a particular therapy modality for a given physiologicstate.
 44. The method of claim 19 wherein a separate estimatedarrhythmia probability is computed for each of a plurality of types ofarrhythmias.
 45. A processor-readable storage medium havingprocessor-executable instructions for performing the method recited inclaim
 1. 46. A processor-readable storage medium havingprocessor-executable instructions for performing the method recited inclaim
 19. 47. A processor-readable storage medium havingprocessor-executable instructions for performing the method recited inclaim
 33. 48. A cardiac rhythm management system, including: a sensingmodule for detecting conditioning events statistically associated withoccurrences of arrhythmias; and, an arrhythmia prediction module forpredicting the o ccurrence of an arrhythmia within a specifiedprediction time period if an estimated arrhythmia probability exceeds aspecified threshold value, wherein the estimated arrhythmia probabilityis computed from a conditional arrhythmia probability associated withthe conditioning event that is derived from past observations of instances in which the conditioning event occurs alone or together with anarrhythmia within a specified time period.
 49. The system of claim 48wherein the sensing module detects conditioning events selected from agroup consisting of a specific morphology of a waveform representingelectrical activity of the heart, a specific pattern of activation timesof different areas of the heart as sensed by a plurality of electrodes,a specific sequence pattern of heartbeats with respect to time, a valueof a measured physiological variable, and a statistic based upon ahistory of occurrences of conditioning events.
 50. The system of claim48 wherein the sensing module detects a plurality of conditioning eventsstatistically associated with the occurrence of an arrhythmia andfurther wherein the arrhythmia prediction module compares a compositeestimated arrhythmia probability with the threshold value in order topredict the occurrence of an arrhythmia, the composite arrhythmiaprobability being a combination of the estimated arrhythmiaprobabilities associated with each detected conditioning event.
 51. Thesystem of claim 50 wherein the conditional arrhythmia probabilities arebased upon past observations of the occurrences of events andarrhythmias taken from population data.
 52. The system of claim 50further comprising an adaptive processing module for computingconditional arrhythmia probabilities based upon past observations of theoccurrences of events and arrhythmias taken in real-time from aparticular patient.
 53. The system of claim 52 wherein the conditionalarrhythmia probabilities computed by the adaptive processing module arebased initially upon past observations of the occurrences of events andarrhythmias taken from population data and each probability issubsequently updated from a previous value to a present value withobservations taken in real-time from a particular patient.
 54. Thesystem of claim 53 wherein the adaptive processing module tests theamount by which the present value differs from the previous value forstatistical significance before updating a conditional arrhythmiaprobability.
 55. The system of claim 52 wherein the adaptive processingmodule tests the statistical association of a conditional arrhythmiaprobability with real-time observation data and discontinues use of aconditional arrhythmia probability in deriving a composite conditionalprobability if the statistical association is below a specified value.56. The system of claim 48 further comprising a therapy module fordelivering a preventive arrhythmia therapy if the estimated arrhythmiaprobability exceeds a therapy threshold.
 57. The system of claim 56further comprising a preventive therapy control module for selecting aparticular therapy to be delivered from a group of one or more availabletherapy modalities.
 58. The system of claim 57 wherein the group ofavailable therapy modalities includes at least one of: delivery of oneor more of a plurality of pharmacological agents, cardiac pacing in aselected pacing mode, delivery of cardioversion/defibrillation shocks,and neural stimulation.
 59. The system of claim 57 wherein the selectionof a particular therapy to be delivered by the therapy control module isbased upon ascertaining a physiologic state of the patient and decidingwhether or not a particular available modality of therapy is appropriatefor delivery to the patient in that physiologic state.
 60. The system ofclaim 59 wherein the physiologic state ascertained for selecting atherapy modality by the therapy control module includes at least one of:the prediction time period of the estimated arrhythmia probability, theparticular conditioning events used to make the arrhythmia prediction,the presence or not of specific conditioning events within a specifiedprior time period, and the magnitude of a measured physiologic variable.61. The system of claim 60 wherein the therapy control module decideswhether or not to deliver a particular mode of preventive therapy byperforming a matrix mapping of a list of detected conditioning events toa specific therapy or therapies considered most appropriate forpreventing an arrhythmia in that physiologic state, wherein the matrixmapping is performed using a translation matrix containing informationrelating to the appropriateness of a particular therapy modality for agiven detected conditioning event, and further wherein the mappingresults in a score for each therapy modality represented in the matrixthat is indicative of the appropriateness of each such therapy modalitygiven the list of detected conditioning events, the score beingevaluated by the therapy control module.
 62. The system of claim 60wherein the therapy control module decides whether or not to deliver aparticular mode of preventive therapy by performing matrix mapping of astate vector representing a specific physiologic state to a specifictherapy or therapies considered most appropriate for preventing anarrhythmia in that physiologic state, wherein the matrix mapping isperformed using a therapy matrix containing information relating to theappropriateness of a particular therapy modality for a given physiologicstate.